How does AI help doctors diagnose disease

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Using AI to make money is practical and many people are doing it successfully right now. Here are realistic approaches: Content creation at scale: use ChatGPT for drafting blog posts, product descriptions, social media content, and email newsletters. Freelance writers who use AI can take on 3-4x the workload while maintaining quality. The key is editing AI output heavily — raw AI content is detectable and lacks the human specificity that makes content excellent. AI-assisted coding: developers using Copilot or Cursor report completing tasks 30-50% faster. This directly increases billable hours. Non-developers can now prototype simple tools and automation scripts using AI-generated code. AI tools reselling: build simple AI-powered tools for specific niches and sell them as SaaS. A legal document summariser, a real estate listing writer, a job description generator for HR departments. Tools built using the OpenAI API can be quite simple technically but solve real problems. Content repurposing services: many businesses need their long-form content turned into social media posts, email sequences, and video scripts. AI makes this fast enough to do profitably at scale.

by ruperthall10757 · 36 upvotes

Building an AI chatbot for your business is now more accessible than ever, and it can genuinely transform customer service. Here's how to approach it practically: For simple FAQ bots, no coding is required. Platforms like Tidio, Intercom, or Crisp offer visual chatbot builders where you define conversation flows. You describe customer questions and provide responses. Setup takes a few hours. For more sophisticated AI that can answer questions using your actual business content, use a RAG (Retrieval Augmented Generation) approach. You upload your documentation, FAQs, product information, and policies. The AI retrieves relevant information from your documents before responding, grounding it in your actual content rather than general training data. Platforms like Voiceflow, Botpress, and Dante AI make RAG-based chatbots accessible without heavy engineering. You can build and deploy one for £50-200/month. For custom development, the OpenAI API or Anthropic's Claude API gives developers a foundation. This allows deeper integration with your existing systems — CRM, order database, booking systems. Key advice: define the chatbot's scope narrowly and hand off to humans clearly when queries exceed that scope.

by farissaleh11468 · 5 upvotes

Prompt engineering is the skill of crafting inputs to AI systems to get the best possible outputs. As AI tools become more embedded in professional workflows, knowing how to communicate with them effectively has genuine value. Key principles: be specific about what you want. "Write a blog post" produces generic output. "Write a 600-word blog post for a Nigerian fintech startup targeting first-time investors aged 25-35, in a conversational but authoritative tone, covering three reasons why index funds beat stock picking, with a call to action to download our app" produces something usable. Provide context and constraints. Tell the model who it is, who the audience is, the format you want, the length, the tone, and what to avoid. The more context, the better the output. Use chain-of-thought for complex reasoning tasks: "Think step by step" significantly improves accuracy on analytical problems. Iterate rather than trying to get perfection in one prompt. Ask it to critique its own output, to rewrite in a different style, or to expand specific sections. Treat it as a collaborative drafting process. For coding: always ask it to explain what the code does, test edge cases, and identify potential bugs in its own output.

by lilyclark